11 citations · 23 across the 16 of their papers we have counts for
16 papers
MiA-Signature: Approximating Global Activation for Long-Context Understanding
Yuqing Li, Jiangnan Li, Mo Yu +3
A growing body of work in cognitive science suggests that reportable conscious access is associated with \emph{global ignition} over distributed memory systems, while such activati…
ExDR: Explanation-driven Dynamic Retrieval Enhancement for Multimodal Fake News Detection
Guoxuan Ding, Yuqing Li, Ziyan Zhou +3
The rapid spread of multimodal fake news poses a serious societal threat, as its evolving nature and reliance on timely factual details challenge existing detection methods. Dynami…
Think out Loud: Emotion Deducing Explanation in Dialogues
Jiangnan Li, Zheng Lin, Lanrui Wang +6
Humans convey emotions through daily dialogues, making emotion understanding a crucial step of affective intelligence. To understand emotions in dialogues, machines are asked to re…
Previously on the Stories: Recap Snippet Identification for Story Reading
Jiangnan Li, Qiujing Wang, Liyan Xu +5
Similar to the "previously-on" scenes in TV shows, recaps can help book reading by recalling the readers' memory about the important elements in previous texts to better understand…
Fine-Grained Modeling of Narrative Context: A Coherence Perspective via Retrospective Questions
Liyan Xu, Jiangnan Li, Mo Yu +1
This work introduces an original and practical paradigm for narrative comprehension, stemming from the characteristics that individual passages within narratives tend to be more co…
SIG: Speaker Identification in Literature via Prompt-Based Generation
Zhenlin Su, Liyan Xu, Jin Xu +2
Identifying speakers of quotations in narratives is an important task in literary analysis, with challenging scenarios including the out-of-domain inference for unseen speakers, an…
Plot Retrieval as an Assessment of Abstract Semantic Association
Shicheng Xu, Liang Pang, Jiangnan Li +5
Retrieving relevant plots from the book for a query is a critical task, which can improve the reading experience and efficiency of readers. Readers usually only give an abstract an…
Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inference
Lanrui Wang, Jiangnan Li, Chenxu Yang +6
Recently, there has been a heightened interest in building chatbots based on Large Language Models (LLMs) to emulate human-like qualities in multi-turn conversations. Despite havin…
Multi-level Adaptive Contrastive Learning for Knowledge Internalization in Dialogue Generation
Chenxu Yang, Zheng Lin, Lanrui Wang +6
Knowledge-grounded dialogue generation aims to mitigate the issue of text degeneration by incorporating external knowledge to supplement the context. However, the model often fails…
Personality Understanding of Fictional Characters during Book Reading
Mo Yu, Jiangnan Li, Shunyu Yao +5
Comprehending characters' personalities is a crucial aspect of story reading. As readers engage with a story, their understanding of a character evolves based on new events and inf…
Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection
Lanrui Wang, Jiangnan Li, Zheng Lin +4
Empathy, which is widely used in psychological counselling, is a key trait of everyday human conversations. Equipped with commonsense knowledge, current approaches to empathetic re…
Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehension
Jiangnan Li, Mo Yu, Fandong Meng +4
In this work, we focus on dialogue reading comprehension (DRC), a task extracting answer spans for questions from dialogues. Dialogue context modeling in DRC is tricky due to compl…
A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models
Yuanxin Liu, Fandong Meng, Zheng Lin +5
Despite the remarkable success of pre-trained language models (PLMs), they still face two challenges: First, large-scale PLMs are inefficient in terms of memory footprint and compu…
Neutral Utterances are Also Causes: Enhancing Conversational Causal Emotion Entailment with Social Commonsense Knowledge
Jiangnan Li, Fandong Meng, Zheng Lin +5
Conversational Causal Emotion Entailment aims to detect causal utterances for a non-neutral targeted utterance from a conversation. In this work, we build conversations as graphs t…
A Hierarchical Transformer with Speaker Modeling for Emotion Recognition in Conversation
Jiangnan Li, Zheng Lin, Peng Fu +2
Emotion Recognition in Conversation (ERC) is a more challenging task than conventional text emotion recognition. It can be regarded as a personalized and interactive emotion recogn…
Learning Class-Transductive Intent Representations for Zero-shot Intent Detection
Qingyi Si, Yuanxin Liu, Peng Fu +3
Zero-shot intent detection (ZSID) aims to deal with the continuously emerging intents without annotated training data. However, existing ZSID systems suffer from two limitations: 1…